prompt caching

**Prompt caching** is the **technique that stores reusable prompt processing artifacts so repeated prompts can skip full prefill computation** - it accelerates inference for recurring instructions and template-based workloads. **What Is Prompt caching?** - **Definition**: Caching mechanism for prompt-level states such as tokenization outputs and KV prefixes. - **Cache Granularity**: Can cache full prompts, shared prefixes, or structured prompt fragments. - **Validity Constraints**: Entries depend on model, tokenizer, and prompt template versions. - **Pipeline Placement**: Applied before decode token generation in serving runtimes. **Why Prompt caching Matters** - **First-Token Speed**: Cached prefills reduce delay before streamed output begins. - **Compute Efficiency**: Removes repeated prefill work for frequently used prompts. - **Scalability**: High cache-hit traffic supports larger request volumes on fixed hardware. - **Cost Management**: Lower duplicate compute improves inference economics. - **UX Consistency**: Repeated workflows become faster and more stable for users. **How It Is Used in Practice** - **Key Strategy**: Use canonicalized prompt fingerprints and context metadata as cache keys. - **Invalidation Rules**: Evict or refresh entries on model updates and policy changes. - **Performance Tracking**: Measure hit rate, stale incidents, and latency impact by endpoint. Prompt caching is **a practical acceleration layer in production LLM serving** - effective prompt caching reduces prefill overhead and improves interactive responsiveness.